{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "718464bc-d3bc-4231-ae00-2a3bff2fab68",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import xgboost as xgb\n",
    "import seaborn as sns \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import numpy as np\n",
    "np.int = int\n",
    "import matplotlib.pyplot as plt\n",
    "#导入各种用于评估模型性能和参数优化的函数和类，例如均方误差（MSE）、均绝对误差（MAE）、决定系数（R2 Score）、解释方差分（explained variance score）等\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.metrics import mean_squared_error, r2_score,explained_variance_score,mean_absolute_percentage_error,mean_absolute_error\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "#导入XGBoost库中的XGBClassifier类，用于建立XGBoost分类模型，并导入plot_importance函数用于特征重要性可视化。\n",
    "from xgboost import XGBClassifier, plot_importance\n",
    "#导入互信息函数\n",
    "from sklearn.feature_selection import mutual_info_regression\n",
    "#导入lasso模型筛选特征变量\n",
    "from sklearn.linear_model import Lasso\n",
    "#导入Scikit-Optimize库中的贝叶斯优化相关的函数和类，用于超参数调优\n",
    "from skopt import gp_minimize\n",
    "from skopt.space import Real, Integer\n",
    "#导入交叉验证函数，用于评估模型性能\n",
    "from sklearn.model_selection import cross_val_score\n",
    "#导入随机森林回归模型\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "plt.rcParams['font.family'] = 'Times New Roman'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "50045870-09f1-4bd7-b236-4bafb6c90da6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#df = pd.read_excel('指标.xlsx')\n",
    "d = pd.read_excel(r'D:\\work\\暑期工作\\0606_重新处理\\河南省小麦产量预测\\数据\\Feature.xlsx')\n",
    "dd = pd.read_excel(r'D:\\work\\暑期工作\\0606_重新处理\\河南省小麦产量预测\\数据\\Features.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fc42cdc5-0483-4439-98af-3593081eff03",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "d=d.drop(columns=['Unnamed: 0'])\n",
    "dd=dd.drop(columns=['Unnamed: 0'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "59285029-518e-41c4-8d69-a0f2c2da0cba",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NAME</th>\n",
       "      <th>AvgSurfT_inst_chengshu</th>\n",
       "      <th>LST_Day_1km_chousui</th>\n",
       "      <th>LST_Night_1km_fennie</th>\n",
       "      <th>soil_temperature_level_2_kaihua</th>\n",
       "      <th>soil_temperature_level_2_guanjiang</th>\n",
       "      <th>soil_temperature_level_2_shouhuo</th>\n",
       "      <th>soil_temperature_level_3_chousui</th>\n",
       "      <th>soil_temperature_level_3_kaihua</th>\n",
       "      <th>soil_temperature_level_3_guanjiang</th>\n",
       "      <th>...</th>\n",
       "      <th>SVAI_chousui</th>\n",
       "      <th>SVAI_kaihua</th>\n",
       "      <th>WDRVI_bajie</th>\n",
       "      <th>WDRVI_chousui</th>\n",
       "      <th>WDVI_bajie</th>\n",
       "      <th>WDVI_yunsui</th>\n",
       "      <th>WDVI_chousui</th>\n",
       "      <th>WDVI_kaihua</th>\n",
       "      <th>wet_kaihua</th>\n",
       "      <th>亩产</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>410522</td>\n",
       "      <td>301.830072</td>\n",
       "      <td>14770.448120</td>\n",
       "      <td>13653.260039</td>\n",
       "      <td>291.679432</td>\n",
       "      <td>294.485981</td>\n",
       "      <td>301.239969</td>\n",
       "      <td>287.263466</td>\n",
       "      <td>288.780851</td>\n",
       "      <td>290.517675</td>\n",
       "      <td>...</td>\n",
       "      <td>0.509428</td>\n",
       "      <td>0.502203</td>\n",
       "      <td>-0.270294</td>\n",
       "      <td>0.038094</td>\n",
       "      <td>0.312649</td>\n",
       "      <td>0.346212</td>\n",
       "      <td>0.380784</td>\n",
       "      <td>0.355850</td>\n",
       "      <td>-0.090609</td>\n",
       "      <td>7467.000138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>410421</td>\n",
       "      <td>301.881276</td>\n",
       "      <td>14811.998245</td>\n",
       "      <td>13724.830174</td>\n",
       "      <td>291.624652</td>\n",
       "      <td>294.462918</td>\n",
       "      <td>301.229017</td>\n",
       "      <td>287.616328</td>\n",
       "      <td>289.032764</td>\n",
       "      <td>290.784441</td>\n",
       "      <td>...</td>\n",
       "      <td>0.456349</td>\n",
       "      <td>0.444113</td>\n",
       "      <td>-0.220879</td>\n",
       "      <td>0.020097</td>\n",
       "      <td>0.272293</td>\n",
       "      <td>0.321097</td>\n",
       "      <td>0.313054</td>\n",
       "      <td>0.308834</td>\n",
       "      <td>-0.088144</td>\n",
       "      <td>5972.666001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>411726</td>\n",
       "      <td>298.990822</td>\n",
       "      <td>15035.873561</td>\n",
       "      <td>13760.252546</td>\n",
       "      <td>291.141219</td>\n",
       "      <td>293.187955</td>\n",
       "      <td>298.777454</td>\n",
       "      <td>287.204259</td>\n",
       "      <td>288.763693</td>\n",
       "      <td>290.271875</td>\n",
       "      <td>...</td>\n",
       "      <td>0.375751</td>\n",
       "      <td>0.406768</td>\n",
       "      <td>-0.273682</td>\n",
       "      <td>-0.176015</td>\n",
       "      <td>0.255247</td>\n",
       "      <td>0.291260</td>\n",
       "      <td>0.276982</td>\n",
       "      <td>0.277341</td>\n",
       "      <td>-0.107184</td>\n",
       "      <td>5687.791498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>410822</td>\n",
       "      <td>302.326105</td>\n",
       "      <td>14909.855104</td>\n",
       "      <td>13684.031442</td>\n",
       "      <td>291.393057</td>\n",
       "      <td>294.128413</td>\n",
       "      <td>300.920627</td>\n",
       "      <td>286.732262</td>\n",
       "      <td>288.209019</td>\n",
       "      <td>290.136585</td>\n",
       "      <td>...</td>\n",
       "      <td>0.466787</td>\n",
       "      <td>0.392344</td>\n",
       "      <td>-0.305916</td>\n",
       "      <td>0.009030</td>\n",
       "      <td>0.277992</td>\n",
       "      <td>0.313744</td>\n",
       "      <td>0.335973</td>\n",
       "      <td>0.327689</td>\n",
       "      <td>-0.062063</td>\n",
       "      <td>7959.597222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>411082</td>\n",
       "      <td>302.665974</td>\n",
       "      <td>14764.439462</td>\n",
       "      <td>13685.820730</td>\n",
       "      <td>292.812644</td>\n",
       "      <td>295.863324</td>\n",
       "      <td>302.456445</td>\n",
       "      <td>288.378758</td>\n",
       "      <td>289.948855</td>\n",
       "      <td>291.813767</td>\n",
       "      <td>...</td>\n",
       "      <td>0.479437</td>\n",
       "      <td>0.456340</td>\n",
       "      <td>-0.155182</td>\n",
       "      <td>0.027083</td>\n",
       "      <td>0.329961</td>\n",
       "      <td>0.356682</td>\n",
       "      <td>0.342203</td>\n",
       "      <td>0.353976</td>\n",
       "      <td>-0.074036</td>\n",
       "      <td>7662.496889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>411081</td>\n",
       "      <td>302.379726</td>\n",
       "      <td>14864.959928</td>\n",
       "      <td>13716.550674</td>\n",
       "      <td>292.036420</td>\n",
       "      <td>294.927847</td>\n",
       "      <td>301.419777</td>\n",
       "      <td>287.740673</td>\n",
       "      <td>289.256381</td>\n",
       "      <td>291.034992</td>\n",
       "      <td>...</td>\n",
       "      <td>0.438036</td>\n",
       "      <td>0.427608</td>\n",
       "      <td>-0.222630</td>\n",
       "      <td>-0.033628</td>\n",
       "      <td>0.276777</td>\n",
       "      <td>0.307460</td>\n",
       "      <td>0.306785</td>\n",
       "      <td>0.308283</td>\n",
       "      <td>-0.090942</td>\n",
       "      <td>6613.422500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>411424</td>\n",
       "      <td>302.404313</td>\n",
       "      <td>14767.178119</td>\n",
       "      <td>13698.554350</td>\n",
       "      <td>293.142870</td>\n",
       "      <td>296.125191</td>\n",
       "      <td>302.061064</td>\n",
       "      <td>288.739608</td>\n",
       "      <td>290.273988</td>\n",
       "      <td>292.107970</td>\n",
       "      <td>...</td>\n",
       "      <td>0.537977</td>\n",
       "      <td>0.518776</td>\n",
       "      <td>-0.049587</td>\n",
       "      <td>0.143995</td>\n",
       "      <td>0.356591</td>\n",
       "      <td>0.372768</td>\n",
       "      <td>0.380640</td>\n",
       "      <td>0.392420</td>\n",
       "      <td>-0.074186</td>\n",
       "      <td>7579.469809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>411724</td>\n",
       "      <td>300.297286</td>\n",
       "      <td>14800.494981</td>\n",
       "      <td>13753.180467</td>\n",
       "      <td>291.750152</td>\n",
       "      <td>293.718662</td>\n",
       "      <td>299.219180</td>\n",
       "      <td>287.779530</td>\n",
       "      <td>289.413163</td>\n",
       "      <td>290.932713</td>\n",
       "      <td>...</td>\n",
       "      <td>0.511967</td>\n",
       "      <td>0.563379</td>\n",
       "      <td>0.082507</td>\n",
       "      <td>0.149694</td>\n",
       "      <td>0.389395</td>\n",
       "      <td>0.347135</td>\n",
       "      <td>0.345173</td>\n",
       "      <td>0.381115</td>\n",
       "      <td>-0.079914</td>\n",
       "      <td>5754.140236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>411324</td>\n",
       "      <td>300.250438</td>\n",
       "      <td>14870.302144</td>\n",
       "      <td>13755.946153</td>\n",
       "      <td>291.086794</td>\n",
       "      <td>293.369445</td>\n",
       "      <td>300.014684</td>\n",
       "      <td>287.141116</td>\n",
       "      <td>288.584741</td>\n",
       "      <td>290.356689</td>\n",
       "      <td>...</td>\n",
       "      <td>0.480808</td>\n",
       "      <td>0.460888</td>\n",
       "      <td>-0.102704</td>\n",
       "      <td>0.005744</td>\n",
       "      <td>0.309025</td>\n",
       "      <td>0.317261</td>\n",
       "      <td>0.348503</td>\n",
       "      <td>0.307512</td>\n",
       "      <td>-0.097445</td>\n",
       "      <td>5467.128359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>410122</td>\n",
       "      <td>302.101448</td>\n",
       "      <td>14900.316284</td>\n",
       "      <td>13728.293344</td>\n",
       "      <td>292.494994</td>\n",
       "      <td>295.405081</td>\n",
       "      <td>301.660649</td>\n",
       "      <td>288.002027</td>\n",
       "      <td>289.590761</td>\n",
       "      <td>291.427115</td>\n",
       "      <td>...</td>\n",
       "      <td>0.380461</td>\n",
       "      <td>0.390789</td>\n",
       "      <td>-0.356993</td>\n",
       "      <td>-0.172850</td>\n",
       "      <td>0.260427</td>\n",
       "      <td>0.290878</td>\n",
       "      <td>0.283795</td>\n",
       "      <td>0.284526</td>\n",
       "      <td>-0.095178</td>\n",
       "      <td>6637.200003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 66 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       NAME  AvgSurfT_inst_chengshu  LST_Day_1km_chousui   \n",
       "0    410522              301.830072         14770.448120  \\\n",
       "1    410421              301.881276         14811.998245   \n",
       "2    411726              298.990822         15035.873561   \n",
       "3    410822              302.326105         14909.855104   \n",
       "4    411082              302.665974         14764.439462   \n",
       "..      ...                     ...                  ...   \n",
       "97   411081              302.379726         14864.959928   \n",
       "98   411424              302.404313         14767.178119   \n",
       "99   411724              300.297286         14800.494981   \n",
       "100  411324              300.250438         14870.302144   \n",
       "101  410122              302.101448         14900.316284   \n",
       "\n",
       "     LST_Night_1km_fennie  soil_temperature_level_2_kaihua   \n",
       "0            13653.260039                       291.679432  \\\n",
       "1            13724.830174                       291.624652   \n",
       "2            13760.252546                       291.141219   \n",
       "3            13684.031442                       291.393057   \n",
       "4            13685.820730                       292.812644   \n",
       "..                    ...                              ...   \n",
       "97           13716.550674                       292.036420   \n",
       "98           13698.554350                       293.142870   \n",
       "99           13753.180467                       291.750152   \n",
       "100          13755.946153                       291.086794   \n",
       "101          13728.293344                       292.494994   \n",
       "\n",
       "     soil_temperature_level_2_guanjiang  soil_temperature_level_2_shouhuo   \n",
       "0                            294.485981                        301.239969  \\\n",
       "1                            294.462918                        301.229017   \n",
       "2                            293.187955                        298.777454   \n",
       "3                            294.128413                        300.920627   \n",
       "4                            295.863324                        302.456445   \n",
       "..                                  ...                               ...   \n",
       "97                           294.927847                        301.419777   \n",
       "98                           296.125191                        302.061064   \n",
       "99                           293.718662                        299.219180   \n",
       "100                          293.369445                        300.014684   \n",
       "101                          295.405081                        301.660649   \n",
       "\n",
       "     soil_temperature_level_3_chousui  soil_temperature_level_3_kaihua   \n",
       "0                          287.263466                       288.780851  \\\n",
       "1                          287.616328                       289.032764   \n",
       "2                          287.204259                       288.763693   \n",
       "3                          286.732262                       288.209019   \n",
       "4                          288.378758                       289.948855   \n",
       "..                                ...                              ...   \n",
       "97                         287.740673                       289.256381   \n",
       "98                         288.739608                       290.273988   \n",
       "99                         287.779530                       289.413163   \n",
       "100                        287.141116                       288.584741   \n",
       "101                        288.002027                       289.590761   \n",
       "\n",
       "     soil_temperature_level_3_guanjiang  ...  SVAI_chousui  SVAI_kaihua   \n",
       "0                            290.517675  ...      0.509428     0.502203  \\\n",
       "1                            290.784441  ...      0.456349     0.444113   \n",
       "2                            290.271875  ...      0.375751     0.406768   \n",
       "3                            290.136585  ...      0.466787     0.392344   \n",
       "4                            291.813767  ...      0.479437     0.456340   \n",
       "..                                  ...  ...           ...          ...   \n",
       "97                           291.034992  ...      0.438036     0.427608   \n",
       "98                           292.107970  ...      0.537977     0.518776   \n",
       "99                           290.932713  ...      0.511967     0.563379   \n",
       "100                          290.356689  ...      0.480808     0.460888   \n",
       "101                          291.427115  ...      0.380461     0.390789   \n",
       "\n",
       "     WDRVI_bajie  WDRVI_chousui  WDVI_bajie  WDVI_yunsui  WDVI_chousui   \n",
       "0      -0.270294       0.038094    0.312649     0.346212      0.380784  \\\n",
       "1      -0.220879       0.020097    0.272293     0.321097      0.313054   \n",
       "2      -0.273682      -0.176015    0.255247     0.291260      0.276982   \n",
       "3      -0.305916       0.009030    0.277992     0.313744      0.335973   \n",
       "4      -0.155182       0.027083    0.329961     0.356682      0.342203   \n",
       "..           ...            ...         ...          ...           ...   \n",
       "97     -0.222630      -0.033628    0.276777     0.307460      0.306785   \n",
       "98     -0.049587       0.143995    0.356591     0.372768      0.380640   \n",
       "99      0.082507       0.149694    0.389395     0.347135      0.345173   \n",
       "100    -0.102704       0.005744    0.309025     0.317261      0.348503   \n",
       "101    -0.356993      -0.172850    0.260427     0.290878      0.283795   \n",
       "\n",
       "     WDVI_kaihua  wet_kaihua           亩产  \n",
       "0       0.355850   -0.090609  7467.000138  \n",
       "1       0.308834   -0.088144  5972.666001  \n",
       "2       0.277341   -0.107184  5687.791498  \n",
       "3       0.327689   -0.062063  7959.597222  \n",
       "4       0.353976   -0.074036  7662.496889  \n",
       "..           ...         ...          ...  \n",
       "97      0.308283   -0.090942  6613.422500  \n",
       "98      0.392420   -0.074186  7579.469809  \n",
       "99      0.381115   -0.079914  5754.140236  \n",
       "100     0.307512   -0.097445  5467.128359  \n",
       "101     0.284526   -0.095178  6637.200003  \n",
       "\n",
       "[102 rows x 66 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75eb75d7-b0ce-4b35-98b5-91a2ab2470aa",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "### 数据处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bb20839c-6340-44b1-a9e7-3c0c367273c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "删除特征的个数： 0\n",
      "出错特征的个数： 0\n",
      "删除的特征： []\n"
     ]
    }
   ],
   "source": [
    "#############删除缺失率高于05的特征\n",
    "null_list = []\n",
    "error_list = []\n",
    "for col in d.columns:\n",
    "    try:\n",
    "        null_list.append([col,d[col].isnull().sum() / d.shape[0]])\n",
    "    except:\n",
    "        error_list.append(col)\n",
    "null_df = pd.DataFrame(null_list,columns = ['特征名称','缺失率'])\n",
    "del_cols = null_df['特征名称'][null_df['缺失率'] >= 0.5]\n",
    "print('删除特征的个数：',len(del_cols))\n",
    "print('出错特征的个数：',len(error_list))\n",
    "print('删除的特征：',del_cols.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "aba9ae28-97f8-4fec-bc66-dd5fbfd318ec",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "删除特征的个数： 0\n",
      "出错特征的个数： 0\n",
      "删除的特征： []\n"
     ]
    }
   ],
   "source": [
    "#############表格中用绿色标记表示删除同值比例高的特征\n",
    "tz_list = []\n",
    "error_list = []\n",
    "for col in d.columns:\n",
    "    try:\n",
    "        tz_list.append([col,d[d[col] == d[col].mode()[0]].shape[0] / (d.shape[0] - d[col].isnull().sum())])\n",
    "    except:\n",
    "        error_list.append(col)\n",
    "tz_df = pd.DataFrame(tz_list,columns = ['特征名称','同值比例'])\n",
    "del_cols = tz_df['特征名称'][tz_df['同值比例'] >= 0.9]\n",
    "print('删除特征的个数：',len(del_cols))\n",
    "print('出错特征的个数：',len(error_list))\n",
    "print('删除的特征：',del_cols.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4032c90c-2e9f-47ff-9b43-383d7419a1d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "d=d.drop(columns=['GNDVI_kaihua','NDVI_chousui','OSAVI_chousui','NAME',\n",
    "                   'SVAI_chousui','SR_chousui','AvgSurfT_inst_chengshu',\n",
    "                  'LST_Day_1km_chousui','LST_Night_1km_fennie','soil_temperature_level_2_kaihua','soil_temperature_level_2_shouhuo',\n",
    "                 'soil_temperature_level_3_chousui','SoilMoi0_10cm_inst_chousui','soil_temperature_level_3_kaihua',\n",
    "                  'SoilMoi0_10cm_inst_kaihua','soil_temperature_level_2_guanjiang','SoilMoi10_40cm_inst_chousui',\n",
    "                  'soil_temperature_level_3_guanjiang','SoilMoi0_10cm_inst_guanjiang','SoilMoi10_40cm_inst_guanjiang',\n",
    "                  'SoilMoi10_40cm_inst_kaihua','soil_temperature_level_1_guanjiang'])\n",
    "dd=dd.drop(columns=['GNDVI_kaihua','NDVI_chousui','OSAVI_chousui','NAME',\n",
    "                   'SVAI_chousui','SR_chousui','AvgSurfT_inst_chengshu',\n",
    "                  'LST_Day_1km_chousui','LST_Night_1km_fennie','soil_temperature_level_2_kaihua','soil_temperature_level_2_shouhuo',\n",
    "                 'soil_temperature_level_3_chousui','SoilMoi0_10cm_inst_chousui','soil_temperature_level_3_kaihua',\n",
    "                  'SoilMoi0_10cm_inst_kaihua','soil_temperature_level_2_guanjiang','SoilMoi10_40cm_inst_chousui',\n",
    "                  'soil_temperature_level_3_guanjiang','SoilMoi0_10cm_inst_guanjiang','SoilMoi10_40cm_inst_guanjiang',\n",
    "                  'SoilMoi10_40cm_inst_kaihua','soil_temperature_level_1_guanjiang'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4a117973-8634-4679-8251-f818d42ab8c0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#d=d.drop(columns=['GNDVI_chousui','SR_yunsui','WDRVI_chousui','SVAI_chousui','SR_chousui'])\n",
    "#dd=dd.drop(columns=['GNDVI_chousui','SR_yunsui','WDRVI_chousui','SVAI_chousui','SR_chousui'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "299d2aaa-7024-4510-a82e-7a8c7e2bce87",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "### 全部入模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0fc106ad-358a-451e-bbdf-57729747d20a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "x_train =d.drop(columns = [\"亩产\"])\n",
    "y_train = d['亩产']\n",
    "x_test =dd.drop(columns = [\"亩产\"])\n",
    "y_test = dd['亩产']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ef9e4a27-8669-49ea-b699-edba291bb268",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "model = xgb.XGBRegressor(n_estimators=150,\n",
    "                                 max_depth=5,\n",
    "                                 learning_rate=0.1,\n",
    "                                 \n",
    "                                 random_state=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "44180510-e0eb-449d-bc96-8b985e0ca0c3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "xgboost_model_fit = model.fit(x_train, y_train)\n",
    "y_pred_bo = xgboost_model_fit.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "136d8f93-7a6c-4694-bbb0-cbc45073abd5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>平均绝对误差（MAPE）</th>\n",
       "      <th>平均绝对误差（MAE）</th>\n",
       "      <th>均方根误差（RMSE）</th>\n",
       "      <th>决定系数（R^2）</th>\n",
       "      <th>解释方差</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>xgboost</th>\n",
       "      <td>0.097841</td>\n",
       "      <td>563.17443</td>\n",
       "      <td>756.56369</td>\n",
       "      <td>0.682313</td>\n",
       "      <td>0.69913</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         平均绝对误差（MAPE）  平均绝对误差（MAE）  均方根误差（RMSE）  决定系数（R^2）     解释方差\n",
       "xgboost      0.097841    563.17443    756.56369   0.682313  0.69913"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 平均绝对百分比误差\n",
    "mape=mean_absolute_percentage_error(y_test,y_pred_bo)\n",
    "# 计算均方根误差（RMSE）\n",
    "rmse = mean_squared_error(y_test, y_pred_bo, squared=False)\n",
    "# 计算平均绝对误差（MAE）\n",
    "mae = mean_absolute_error(y_test, y_pred_bo)\n",
    "# 计算决定系数（R^2）\n",
    "r2 = r2_score(y_test, y_pred_bo)\n",
    "# 计算解释方差分\n",
    "explained_var = explained_variance_score(y_test, y_pred_bo)\n",
    "\n",
    "results1 = pd.DataFrame()\n",
    "results1['平均绝对误差（MAPE）'] = [mape]\n",
    "results1['平均绝对误差（MAE）'] = [mae]\n",
    "results1['均方根误差（RMSE）'] = [rmse]\n",
    "results1['决定系数（R^2）'] = [r2]\n",
    "results1['解释方差'] = [explained_var]\n",
    "results1.index = ['xgboost']\n",
    "results1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "43a37742-b0a1-4ac8-9f09-39d1fe7992bf",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y_test</th>\n",
       "      <th>y_pred</th>\n",
       "      <th>precent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7467.000138</td>\n",
       "      <td>7457.051758</td>\n",
       "      <td>-0.13%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5972.666001</td>\n",
       "      <td>7284.182129</td>\n",
       "      <td>21.96%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5687.791498</td>\n",
       "      <td>4852.651855</td>\n",
       "      <td>-14.68%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7959.597222</td>\n",
       "      <td>7561.193848</td>\n",
       "      <td>-5.01%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7662.496889</td>\n",
       "      <td>7395.193359</td>\n",
       "      <td>-3.49%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>6613.422500</td>\n",
       "      <td>7189.296875</td>\n",
       "      <td>8.71%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>7579.469809</td>\n",
       "      <td>7423.167480</td>\n",
       "      <td>-2.06%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>5754.140236</td>\n",
       "      <td>7513.714844</td>\n",
       "      <td>30.58%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>5467.128359</td>\n",
       "      <td>6300.725098</td>\n",
       "      <td>15.25%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>6637.200003</td>\n",
       "      <td>5685.078613</td>\n",
       "      <td>-14.35%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          y_test       y_pred  precent\n",
       "0    7467.000138  7457.051758   -0.13%\n",
       "1    5972.666001  7284.182129   21.96%\n",
       "2    5687.791498  4852.651855  -14.68%\n",
       "3    7959.597222  7561.193848   -5.01%\n",
       "4    7662.496889  7395.193359   -3.49%\n",
       "..           ...          ...      ...\n",
       "97   6613.422500  7189.296875    8.71%\n",
       "98   7579.469809  7423.167480   -2.06%\n",
       "99   5754.140236  7513.714844   30.58%\n",
       "100  5467.128359  6300.725098   15.25%\n",
       "101  6637.200003  5685.078613  -14.35%\n",
       "\n",
       "[102 rows x 3 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_array = np.array(y_test)\n",
    "\n",
    "y_pred_series = pd.Series(y_pred_bo, name='y_pred')\n",
    "y_test_series = pd.Series(y_test_array, name='y_test')\n",
    "dftest= pd.concat([y_test_series, y_pred_series], axis=1)\n",
    "dftest['precent'] = ((dftest['y_pred'] - dftest['y_test']) / dftest['y_test']) * 100\n",
    "dftest['precent'] = dftest['precent'].apply(lambda x: '{:.2f}%'.format(x))\n",
    "dftest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6b1994e2-af3b-478a-992d-e4128e188f91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_test_sorted = np.sort(y_test)\n",
    "y_pred_sorted = y_pred_bo[np.argsort(y_test)]\n",
    "\n",
    "# 绘制折线对比图\n",
    "plt.plot(range(len(y_test_sorted)), y_test_sorted, label='Actual')\n",
    "plt.plot(range(len(y_pred_sorted)), y_pred_sorted, label='Predicted',linestyle='--')\n",
    "#plt.xlabel('Index')\n",
    "plt.ylabel('Value',fontsize=16)\n",
    "plt.title('Actual vs. Predicted (Bo-XGBoost)',fontsize=16)\n",
    "plt.legend()\n",
    "plt.ylim(1000, 10000)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "df4a379a-10bb-4f49-8b5f-1490cbda5d25",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "plt.scatter(y_test, y_pred_bo, alpha=0.5, label='Data Points')\n",
    "plt.xlabel('Actual Values')\n",
    "plt.ylabel('Predicted Values')\n",
    "plt.title('bh-XGBoost Scatter Plot of Actual vs. Predicted Values')\n",
    "\n",
    "# 添加对角线\n",
    "max_value = max(max(y_test), max(y_pred_bo))\n",
    "min_value = min(min(y_test), min(y_pred_bo))\n",
    "plt.plot([min_value, max_value], [min_value, max_value], color='red', linestyle='--', linewidth=2, label='Perfect Prediction')\n",
    "\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3896133d-f939-4563-8d25-5aed0e0b5413",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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